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Special Issue "Advances in Modelling of Rainfall Fields"

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A special issue of Hydrology (ISSN 2306-5338).

Deadline for manuscript submissions: 15 January 2022.

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Special Issue Editors

Dr. Davide Luciano De Luca
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Guest Editor

Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria (Italy), Ponte Pietro Bucci, 41b Building, V Floor, 87036 Rende (CS), Italy
Interests: stochastic processes; rainfall fields modelling; mathematics; statistics; GIS; early warning systems

Dr. Andrea Petroselli
E-Mail Website

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Guest Editor

Department of Economics, Engineering, Society and Business Organization (DEIM), Tuscia University, 01100 Viterbo, Italy
Interests: rainfall-runoff modeling; flood prone area estimation; surface hydrology; GIS Terrain Analysis for hydrogeomorphic applications; hydrological processes monitoring and modelling
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Special Issue Information

Dear Colleagues,

Rainfall is the main input for all hydrological models such as, for example, rainfall-runoff models and forecasting of landslides triggered by precipitation. Consequently, the need of improving the modelling of rainfall fields constitutes a key aspect for i) realizing efficient early warning systems and ii) carrying out analyses of future scenarios related to occurrences and magnitudes for all the induced phenomena.

The aim of this Special Issue is to provide a collection of innovative contributions for rainfall modelling, focusing on hydrological scales and on a context of climate changes. In particular, the following topics are of interest:

Statistical analysis of rainfall extremes, mainly focusing on Intensity-Duration-Frequency (IDF) curves and evaluation of Rainfall Thresholds;
Temporal and spatial rainfall distribution;
Transient Stochastic Rainfall Generators, suitable for obtaining long and perturbed time series into a context of climate changes;
Models for rainfall nowcasting at hydrological scales, which can also couple several data sources (from rain gauge networks, weather radar, outputs from Limited Area Models) into a Bayesian framework;
Rainfall downscaling

Dr. Davide Luciano De Luca
Assoc. Prof. Andrea Petroselli
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

IDF curves
Return period
Rainfall thresholds
Temporal and spatial rainfall distribution
Stochastic Rainfall Generators
Bayesian framework
Rainfall nowcasting
Rainfall downscaling

Published Papers (7 papers)

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Research

Open AccessArticle

Integrative Assessment of Stormwater Infiltration Practices in Rapidly Urbanizing Cities: A Case of Lucknow City, India

Jheel Bastia

Binaya Kumar Mishra

and

Pankaj Kumar

Hydrology 2021, 8(2), 93; https://doi.org/10.3390/hydrology8020093 - 12 Jun 2021

Viewed by 799

Abstract

The lack of strategic planning in stormwater management has made rapidly urbanizing cities more vulnerable to urban water issues than in the past. Low infiltration rates, increase in peak river discharge, and recurrence of urban floods and waterlogging are clear signs of unplanned rapid urbanization. As with many other low to middle-income countries, India depends on its conventional and centralized stormwater drains for managing stormwater runoff. However, in the absence of a robust stormwater management policy governed by the state, its impact trickles down to a municipal level and the negative outcome can be clearly observed through a failure of the drainage systems. This study examines the role of onsite and decentralized stormwater infiltration facilities, as successfully adopted by some higher income countries, under physical and social variability in the context of the metropolitan city of Lucknow, India. Considering the 2030 Master Plan of Lucknow city, this study investigated the physical viability of the infiltration facilities. Gridded ModClark rainfall-runoff modeling was carried out in Kukrail river basin, an important drainage basin of Lucknow city. The HEC-HMS model, inside the watershed modeling system (WMS), was used to simulate stormwater runoff for multiple scenarios of land use and rainfall intensities. With onsite infiltration facilities as part of land use measures, the peak discharge reduced in the range of 48% to 59%. Correlation analysis and multiple regression were applied to understand the rainfall-runoff relationship. Furthermore, the stormwater runoff drastically reduced with decentralized infiltration systems. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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Open AccessFeature PaperArticle

STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series

Davide Luciano De Luca

and

Andrea Petroselli

Hydrology 2021, 8(2), 76; https://doi.org/10.3390/hydrology8020076 - 03 May 2021

Cited by 1 | Viewed by 951

Abstract

The MS Excel file with VBA (Visual Basic for Application) macros named STORAGE (STOchastic RAinfall GEnerator) is introduced herein. STORAGE is a temporal stochastic simulator aiming at generating long and high-resolution rainfall time series, and it is based on the implementation of a Neymann–Scott Rectangular Pulse (NSRP) model. STORAGE is characterized by two innovative aspects. First, its calibration (i.e., the parametric estimation, on the basis of available sample data, in order to better reproduce some rainfall features of interest) is carried out by using data series (annual maxima rainfall, annual and monthly cumulative rainfall, annual number of wet days) which are usually longer than observed high-resolution series (that are mainly adopted in literature for the calibration of other stochastic simulators but are usually very short or absent for many rain gauges). Second, the seasonality is modelled using series of goniometric functions. This approach makes STORAGE strongly parsimonious with respect to the use of monthly or seasonal sets for parameters. Applications for the rain gauge network in the Calabria region (southern Italy) are presented and discussed herein. The results show a good reproduction of the rainfall features which are mainly considered for usual hydrological purposes. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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Open AccessArticle

Rainfall-Runoff Modeling Using the HEC-HMS Model for the Al-Adhaim River Catchment, Northern Iraq

Ahmed Naseh Ahmed Hamdan

Suhad Almuktar

and

Miklas Scholz

Hydrology 2021, 8(2), 58; https://doi.org/10.3390/hydrology8020058 - 26 Mar 2021

Cited by 1 | Viewed by 1143

Abstract

It has become necessary to estimate the quantities of runoff by knowing the amount of rainfall to calculate the required quantities of water storage in reservoirs and to determine the likelihood of flooding. The present study deals with the development of a hydrological model named Hydrologic Engineering Center (HEC-HMS), which uses Digital Elevation Models (DEM). This hydrological model was used by means of the Geospatial Hydrologic Modeling Extension (HEC-GeoHMS) and Geographical Information Systems (GIS) to identify the discharge of the Al-Adhaim River catchment and embankment dam in Iraq by simulated rainfall-runoff processes. The meteorological models were developed within the HEC-HMS from the recorded daily rainfall data for the hydrological years 2015 to 2018. The control specifications were defined for the specified period and one day time step. The Soil Conservation Service-Curve number (SCS-CN), SCS Unit Hydrograph and Muskingum methods were used for loss, transformation and routing calculations, respectively. The model was simulated for two years for calibration and one year for verification of the daily rainfall values. The results showed that both observed and simulated hydrographs were highly correlated. The model’s performance was evaluated by using a coefficient of determination of 90% for calibration and verification. The dam’s discharge for the considered period was successfully simulated but slightly overestimated. The results indicated that the model is suitable for hydrological simulations in the Al-Adhaim river catchment. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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Open AccessArticle

Intra-Storm Pattern Recognition through Fuzzy Clustering

Konstantinos Vantas

and

Epaminondas Sidiropoulos

Hydrology 2021, 8(2), 57; https://doi.org/10.3390/hydrology8020057 - 25 Mar 2021

Cited by 1 | Viewed by 768

Abstract

The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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Open AccessArticle

Rainfall Intensity-Duration-Frequency Relationship. Case Study: Depth-Duration Ratio in a Semi-Arid Zone in Mexico

Ena Gámez-Balmaceda

Alvaro López-Ramos

Luisa Martínez-Acosta

Juan Pablo Medrano-Barboza

John Freddy Remolina López

Georges Seingier

Luis Walter Daesslé

and

Alvaro Alberto López-Lambraño

Hydrology 2020, 7(4), 78; https://doi.org/10.3390/hydrology7040078 - 15 Oct 2020

Viewed by 1127

Abstract

Intensity–Duration–Frequency (IDF) curves describe the relationship between rainfall intensity, rainfall duration, and return period. They are commonly used in the design, planning and operation of hydrologic, hydraulic, and water resource systems. Considering the intense rainfall presence with flooding occurrences, limited data used to develop IDF curves, and importance to improve the IDF design for the Ensenada City in Baja California, this research study aims to investigate the use and combinations of pluviograph and daily records, to assess rain behavior around the city, and select a suitable method that provides the best results of IDF relationship, consequently updating the IDF relationship for the city for return periods of 10, 25, 50, and 100 years. The IDF relationship is determined through frequency analysis of rainfall observations. Also, annual maximum rainfall intensity for several duration and return periods has been analyzed according to the statistical distribution of Gumbel Extreme Value (GEV). Thus, Chen’s method was evaluated based on the depth-duration ratio (R) from the zone, and the development of the IDF relationship for the rain gauges stations was focused on estimating the most suitable (R) ratio; chosen from testing several methods and analyzing the rain in the region from California and Baja California. The determined values of the rain for one hour and return period of 2 years

(P_{1}^{2})

obtained were compared to the values of some cities in California and Baja California, with a range between 10 and 16.61 mm, and the values of the (R) ratio are in a range between 0.35 and 0.44; this range is close to the (R) ratio of 0.44 for one station in Tijuana, a city 100 km far from Ensenada. The values found here correspond to the rainfall characteristics of the zone; therefore, the method used in this study can be replicated to other semi-arid zones with the same rain characteristics. Finally, it is suggested that these results of the IDF relationship should be incorporated on the Norm of the State of Baja California as the recurrence update requires it upon recommendation. This study is the starting point to other studies that imply the calculation of a peak flow and evaluation of hydraulic structures as an input to help improve flood resilience in the city of Ensenada. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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Open AccessArticle

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

Héctor A. Ballinas-González

Víctor H. Alcocer-Yamanaka

Javier J. Canto-Rios

and

Roel Simuta-Champo

Hydrology 2020, 7(4), 73; https://doi.org/10.3390/hydrology7040073 - 05 Oct 2020

Cited by 5 | Viewed by 905

Abstract

Rainfall–runoff phenomena are among the main processes within the hydrological cycle. In urban zones, the increases in imperviousness cause increased runoff, originating floods. It is fundamental to know the sensitivity of parameters in the modeling of an urban basin, which makes the calibration process more efficient by allowing one to focus only on the parameters for which the modeling results are sensitive. This research presents a formal sensitivity analysis of hydrological and hydraulic parameters—absolute–relative, relative–absolute, relative–relative sensitivity and R²—applied to an urban basin. The urban basin of Tuxtla Gutiérrez, Chiapas, in Mexico is an area prone to flooding caused by extreme precipitation events. The basin has little information in which the records (with the same time resolution) of precipitation and hydrometry match. The basin model representing an area of 355.07 km² was characterized in the Stormwater Management Model (SWMM). The sensitivity analysis was performed for eight hydrological parameters and one hydraulic for two precipitation events and their impact on the depths of the Sabinal River. Based on the analysis, the parameters derived from the analysis that stand out as sensitive are the Manning coefficient of impervious surface and the minimum infiltration speed with R² > 0.60. The results obtained demonstrate the importance of knowing the sensitivity of the parameters and their selection to perform an adequate calibration. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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Open AccessArticle

Evaluation of MERRA-2 Precipitation Products Using Gauge Observation in Nepal

Mandira Singh Shrestha

Tianli Xu

Dibas Shrestha

and

Binod Dawadi

Hydrology 2020, 7(3), 40; https://doi.org/10.3390/hydrology7030040 - 13 Jul 2020

Cited by 7 | Viewed by 1230

Abstract

Precipitation is the most important variable in the climate system and the dominant driver of land surface hydrologic conditions. Rain gauge measurement provides precipitation estimates on the ground surface; however, these measurements are sparse, especially in the high-elevation areas of Nepal. Reanalysis datasets are the potential alternative for precipitation measurement, although it must be evaluated and validated before use. This study evaluates the performance of second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) datasets with the 141-gauge observations from Nepal between 2000 and 2018 on monthly, seasonal, and annual timescales. Different statistical measures based on the Correlation Coefficient (R), Mean Bias (MB), Root-Mean-Square Error (RMSE), and Nash–Sutcliffe efficiency (NSE) were adopted to determine the performance of both MERRA-2 datasets. The results revealed that gauge calibrated (MERRA-C) underestimated, whereas model-only (MERRA-NC) overestimated the observed seasonal cycle of precipitation. However, both datasets were able to reproduce seasonal precipitation cycle with a high correlation (R ≥ 0.95), as revealed by observation. MERRA-C datasets showed a more consistent spatial performance (higher R-value) to the observed datasets than MERRA-NC, while MERRA-NC is more reasonable to estimate precipitation amount (lower MB) across the country. Both MERRA-2 datasets performed better in winter, post-monsoon, and pre-monsoon than in summer monsoon. Moreover, MERRA-NC overestimated the observed precipitation in mid and high-elevation areas, whereas MERRA-C severely underestimated at most of the stations throughout all seasons. Among both datasets, MERRA-C was only able to reproduce the observed elevation dependency pattern. Furthermore, uncertainties in MERRA-2 precipitation products mentioned above are still worthy of attention by data developers and users. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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